1. Field of the Invention
Generally, the present invention relates to the field of fabricating integrated circuits, and, more particularly, to the monitoring of process tool throughput of a plurality of process tools required for processing different products with different process recipes.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of semiconductor fabrication, since here it is essential to combine cutting edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improving process tool utilization. The latter aspect is especially important, since modern semiconductor facilities equipment is required, which is extremely cost-intensive and represents the dominant part of the total production costs.
Integrated circuits are typically manufactured in automated or semi-automated facilities, thereby passing through a large number of process and metrology steps to complete the device. The number and the type of process steps and metrology steps a semiconductor device has to go through depends on the specifics of the semiconductor device to be fabricated. A usual process flow for an integrated circuit may include a plurality of photolithography steps to image a circuit pattern for a specific device layer into a resist layer, which is subsequently patterned to form a resist mask for further processes in structuring the device layer under consideration by, for example, etch or implant processes and the like. Thus, layer after layer, a plurality of process steps are performed based on a specific lithographic mask set for the various layers of the specified device. For instance, a sophisticated CPU requires several hundred process steps, each of which has to be carried out within specified process margins to fulfill the specifications for the device under consideration. Since many of these processes are very critical, a plurality of metrology steps have to be performed to efficiently control the process flow. Typical metrology processes may include the measurement of layer thickness, the determination of dimensions of critical features, such as the gate length of transistors, the measurement of dopant profiles, and the like. As the majority of the process margins are device specific, many of the metrology processes and the actual manufacturing processes are specifically designed for the device under consideration and require specific parameter settings at the adequate metrology and process tools.
In a semiconductor facility, usually a plurality of different product types are manufactured at the same time, such as memory chips of different design and storage capacity, CPUs of different design and operating speed, and the like, wherein the number of different product types may even reach a hundred and more in production lines for manufacturing ASICs (application specific ICs). Since each of the different product types may require a specific process flow, different mask sets for the lithography, specific settings in the various process tools, such as deposition tools, etch tools, implantation tools, CMP (chemical mechanical polishing) tools, and the like, may be necessary. Consequently, a plurality of different tool parameter settings and product types may be encountered simultaneously in a manufacturing environment.
Hereinafter, the parameter setting for a specific process in a specified process tool or metrology or inspection tool may commonly be referred to as a process recipe or simply as a recipe. Thus, a large number of different process recipes, even for the same type of process tools, may be required which have to be applied to the process tools at the time the corresponding product types are to be processed in the respective tools. However, the sequence of process recipes performed in process and metrology tools or in functionally combined equipment groups, as well as the recipes themselves, may have to be frequently altered due to fast product changes and highly variable processes involved. As a consequence, the tool performance, especially in terms of throughput, is a very critical manufacturing parameter as it significantly affects the overall production costs of the individual devices. The progression of throughput over time of individual process and metrology tools, or even certain entities thereof such as process modules, substrate robot handlers, load ports and the like, may, however, remain unobserved due to the complexity of the manufacturing sequences including a large number of product types and a corresponding large number of processes, which in turn are subjected to frequent recipe changes. Hence, low-performing tools may remain undetected for a long time, when the performance of an equipment group which the tool under consideration belongs to is within its usual performance margin that typically has to be selected to allow a relatively wide span of variations, owing to the complexity of the processes and the tools involved.
Ideally, the effect of each individual process on each substrate would be detected by measurement and the substrate under consideration would be released for further process only if the required specifications were met. A corresponding process control in view of the result of each individual process, however, is not practical, since measuring the effects of certain processes may require relatively long measurement times or may even necessitate the destruction of the sample. Moreover, immense effort in terms of time and equipment would have to be made on the metrology side to provide the required measurement results. Additionally, utilization of the process tools involved would be minimized, since the tool would be released only after the provision of the measurement result and its assessment.
Typically, due to the lack of “knowledge” regarding the effect of the individual processes owing to the restricted metrology capability, statistical methods, such as mean values and corresponding standard deviations and the like, have been introduced for adjusting process parameters to significantly relax the above problem and to allow a moderately high utilization of the process tools, while at the same time attaining a relatively high product yield. Moreover, a process control strategy has been introduced and is continuously improved which allows a high degree of process control, desirably on a run-to-run basis, without the necessity of an immediate response of a measurement tool. In this control strategy, the so-called advanced process control, a model of a process or of a group of inter-related processes, is established and implemented in an appropriately configured process controller. Based on a certain amount of measurement results of preceding and/or subsequent processes, a feed forward and/or feedback control loop is established to maintain the process variability within predefined tolerances. Although well-established statistical process control (SPC) mechanisms, in combination with advanced process control (APC) strategies, provide the potential for achieving a high degree of quality of the products on the basis of a restricted amount of process information, these strategies may not sufficiently take into consideration other performance criteria of a process line, such as the throughput of the process tools involved. For instance, the malfunction of an entity of a process tool, such as a process module, a substrate handler robot, a load port and the like, may not necessarily severely compromise the quality of the substrates processed, but may in a more or less subtle manner influence the overall throughput of the process tool or a group of process tools. Similarly, process changes and/or setup changes of one or more process tools, which may be performed to take into account process variations and/or to improve results of individual processes, may even promote an enhanced quality of the result of the process or processes under consideration, but may result in a reduced throughput owing to, for instance, increased robot activities, additional recipe steps and the like. Hence, monitoring of the performance of equipment and equipment groups with respect to throughput efficiency is highly complex, and even throughput studies at entity level, i.e., monitoring some or all of the individual entities comprising a specified process tool, such as process modules, robot handlers, load ports and the like, may represent a less attractive solution, since resources in operations and industrial engineering are limited and an immediate response to the throughput changes may not be practical, even though the throughput studies may reveal certain details regarding one or more specified process tools.
In view of the situation described above, there is therefore a need for an enhanced technique that enables enhancement of the efficiency of a semiconductor production process, especially in view of throughput related issues.